"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.



Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Imported from: https://github.com/LiyuanLucasLiu/RAdam

Paper: https://arxiv.org/abs/1908.03265

@article{liu2019radam,
  title={On the Variance of the Adaptive Learning Rate and Beyond},
  author={Liu, Liyuan and Jiang, Haoming and He, Pengcheng and Chen, Weizhu and Liu, Xiaodong and Gao, Jianfeng and Han, Jiawei},
  journal={arXiv preprint arXiv:1908.03265},
  year={2019}
}
"""
from __future__ import print_function, absolute_import
import math
import torch
from torch.optim.optimizer import Optimizer


class RAdam(Optimizer):

    def __init__(
        self,
        params,
        lr=1e-3,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0,
        degenerated_to_sgd=True
    ):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= eps:
            raise ValueError("Invalid epsilon value: {}".format(eps))
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError(
                "Invalid beta parameter at index 0: {}".format(betas[0])
            )
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError(
                "Invalid beta parameter at index 1: {}".format(betas[1])
            )

        self.degenerated_to_sgd = degenerated_to_sgd
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
        self.buffer = [[None, None, None] for ind in range(10)]
        super(RAdam, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(RAdam, self).__setstate__(state)

    def step(self, closure=None):

        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:

            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data.float()
                if grad.is_sparse:
                    raise RuntimeError(
                        'RAdam does not support sparse gradients'
                    )

                p_data_fp32 = p.data.float()

                state = self.state[p]

                if len(state) == 0:
                    state['step'] = 0
                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
                else:
                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(
                        p_data_fp32
                    )

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                beta1, beta2 = group['betas']

                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                exp_avg.mul_(beta1).add_(1 - beta1, grad)

                state['step'] += 1
                buffered = self.buffer[int(state['step'] % 10)]
                if state['step'] == buffered[0]:
                    N_sma, step_size = buffered[1], buffered[2]
                else:
                    buffered[0] = state['step']
                    beta2_t = beta2**state['step']
                    N_sma_max = 2 / (1-beta2) - 1
                    N_sma = N_sma_max - 2 * state['step'
                                                  ] * beta2_t / (1-beta2_t)
                    buffered[1] = N_sma

                    # more conservative since it's an approximated value
                    if N_sma >= 5:
                        step_size = math.sqrt(
                            (1-beta2_t) * (N_sma-4) / (N_sma_max-4) *
                            (N_sma-2) / N_sma * N_sma_max / (N_sma_max-2)
                        ) / (1 - beta1**state['step'])
                    elif self.degenerated_to_sgd:
                        step_size = 1.0 / (1 - beta1**state['step'])
                    else:
                        step_size = -1
                    buffered[2] = step_size

                # more conservative since it's an approximated value
                if N_sma >= 5:
                    if group['weight_decay'] != 0:
                        p_data_fp32.add_(
                            -group['weight_decay'] * group['lr'], p_data_fp32
                        )
                    denom = exp_avg_sq.sqrt().add_(group['eps'])
                    p_data_fp32.addcdiv_(
                        -step_size * group['lr'], exp_avg, denom
                    )
                    p.data.copy_(p_data_fp32)
                elif step_size > 0:
                    if group['weight_decay'] != 0:
                        p_data_fp32.add_(
                            -group['weight_decay'] * group['lr'], p_data_fp32
                        )
                    p_data_fp32.add_(-step_size * group['lr'], exp_avg)
                    p.data.copy_(p_data_fp32)

        return loss


class PlainRAdam(Optimizer):

    def __init__(
        self,
        params,
        lr=1e-3,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0,
        degenerated_to_sgd=True
    ):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= eps:
            raise ValueError("Invalid epsilon value: {}".format(eps))
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError(
                "Invalid beta parameter at index 0: {}".format(betas[0])
            )
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError(
                "Invalid beta parameter at index 1: {}".format(betas[1])
            )

        self.degenerated_to_sgd = degenerated_to_sgd
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)

        super(PlainRAdam, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(PlainRAdam, self).__setstate__(state)

    def step(self, closure=None):

        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:

            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data.float()
                if grad.is_sparse:
                    raise RuntimeError(
                        'RAdam does not support sparse gradients'
                    )

                p_data_fp32 = p.data.float()

                state = self.state[p]

                if len(state) == 0:
                    state['step'] = 0
                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
                else:
                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(
                        p_data_fp32
                    )

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                beta1, beta2 = group['betas']

                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                exp_avg.mul_(beta1).add_(1 - beta1, grad)

                state['step'] += 1
                beta2_t = beta2**state['step']
                N_sma_max = 2 / (1-beta2) - 1
                N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1-beta2_t)

                # more conservative since it's an approximated value
                if N_sma >= 5:
                    if group['weight_decay'] != 0:
                        p_data_fp32.add_(
                            -group['weight_decay'] * group['lr'], p_data_fp32
                        )
                    step_size = group['lr'] * math.sqrt(
                        (1-beta2_t) * (N_sma-4) / (N_sma_max-4) *
                        (N_sma-2) / N_sma * N_sma_max / (N_sma_max-2)
                    ) / (1 - beta1**state['step'])
                    denom = exp_avg_sq.sqrt().add_(group['eps'])
                    p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
                    p.data.copy_(p_data_fp32)
                elif self.degenerated_to_sgd:
                    if group['weight_decay'] != 0:
                        p_data_fp32.add_(
                            -group['weight_decay'] * group['lr'], p_data_fp32
                        )
                    step_size = group['lr'] / (1 - beta1**state['step'])
                    p_data_fp32.add_(-step_size, exp_avg)
                    p.data.copy_(p_data_fp32)

        return loss


class AdamW(Optimizer):

    def __init__(
        self,
        params,
        lr=1e-3,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0,
        warmup=0
    ):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= eps:
            raise ValueError("Invalid epsilon value: {}".format(eps))
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError(
                "Invalid beta parameter at index 0: {}".format(betas[0])
            )
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError(
                "Invalid beta parameter at index 1: {}".format(betas[1])
            )

        defaults = dict(
            lr=lr,
            betas=betas,
            eps=eps,
            weight_decay=weight_decay,
            warmup=warmup
        )
        super(AdamW, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(AdamW, self).__setstate__(state)

    def step(self, closure=None):
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:

            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data.float()
                if grad.is_sparse:
                    raise RuntimeError(
                        'Adam does not support sparse gradients, please consider SparseAdam instead'
                    )

                p_data_fp32 = p.data.float()

                state = self.state[p]

                if len(state) == 0:
                    state['step'] = 0
                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
                else:
                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(
                        p_data_fp32
                    )

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                beta1, beta2 = group['betas']

                state['step'] += 1

                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                exp_avg.mul_(beta1).add_(1 - beta1, grad)

                denom = exp_avg_sq.sqrt().add_(group['eps'])
                bias_correction1 = 1 - beta1**state['step']
                bias_correction2 = 1 - beta2**state['step']

                if group['warmup'] > state['step']:
                    scheduled_lr = 1e-8 + state['step'] * group['lr'] / group[
                        'warmup']
                else:
                    scheduled_lr = group['lr']

                step_size = scheduled_lr * math.sqrt(
                    bias_correction2
                ) / bias_correction1

                if group['weight_decay'] != 0:
                    p_data_fp32.add_(
                        -group['weight_decay'] * scheduled_lr, p_data_fp32
                    )

                p_data_fp32.addcdiv_(-step_size, exp_avg, denom)

                p.data.copy_(p_data_fp32)

        return loss
